class_docstr: Quantizer for handling weights in Conv2DTranspose layers.
docstr: Construct LastValueQuantizer with params specific for TFLite Conv2DTranpose.
docstr_tok: ["Construct","LastValueQuantizer","with","params","specific","for","TFLite","Conv2DTranpose","."]
code: def __init__(
      self, num_bits_weight: int = 8, num_bits_activation: int = 8
  ):
      """Construct LastValueQuantizer with params specific for TFLite Conv2DTranpose."""
      super(DefaultNBitConvTransposeWeightsQuantizer, self).__init__(
          num_bits=num_bits_weight,
          per_axis=False,
          symmetric=True,
          narrow_range=True,
      )  # weight
      self._num_bits_weight = num_bits_weight
      self._num_bits_activation = num_bits_activation
  
file_path: model-optimization/tensorflow_model_optimization/python/core/quantization/keras/experimental/default_n_bit/default_n_bit_quantizers.py
imports: import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
label_desc: Use image node_id 1 to create a new DefaultNBitConvTransposeWeightsQuantizer object from inherited base classes: quantizers with example: obj = DefaultNBitConvTransposeWeightsQuantizer(num_bits_weight, num_bits_activation)